IGES_sc_workshop
2023-11-05
Tutorial goals:
The following tutorial is designed to give an overview of data analyses using single cell sequencing datasets using the Seurat procedure. Here, we address three main goals:
- Perform QC analysis as well as dimensionality reduction
- Identify cell types markers and cell type clusters that are present in both datasets
- Compare the datasets to find cell-type and condition specific signatures
Download single cell raw count matrices
The original data is collected from this paper - Melms, J.C., Biermann, J., Huang, H. et al. A molecular single-cell lung atlas of lethal COVID-19. Nature 595, 114–119 (2021). https://doi.org/10.1038/s41586-021-03569-1
N.B.For this workshop, we have subsetted the control(C51ctr,C53ctr,C56ctr) & cases(L11cov, L22cov, L07cov) samples. Please download the smaller dataset and metadata from: https://github.com/gamazonlab/IGESWorkshop2023.git Once downloaded onto local computer, create a folder, move the files to the folder andunzip them.
# Load libraries
# If not already installed, please install first
library(Seurat)
library(data.table)
library(dplyr)
library(patchwork)
library(cowplot)
library(patchwork)
library(multtest)
library(metap)
library(ggplot2)
library(enrichR)Read in the count matrices for both cases and control samples
# change to data.frames and merge the two tables
cases <- fread("cases.count.matrix.txt")## Warning in fread("cases.count.matrix.txt"): Detected 15357 column names but the
## data has 15358 columns (i.e. invalid file). Added 1 extra default column name
## for the first column which is guessed to be row names or an index. Use
## setnames() afterwards if this guess is not correct, or fix the file write
## command that created the file to create a valid file.cases <- as.data.frame(cases)
row.names(cases) <- cases$V1
cases <- cases[,-c(1)]
controls <- fread("control.count.matrices.txt")## Warning in fread("control.count.matrices.txt"): Detected 17308 column names but
## the data has 17309 columns (i.e. invalid file). Added 1 extra default column
## name for the first column which is guessed to be row names or an index. Use
## setnames() afterwards if this guess is not correct, or fix the file write
## command that created the file to create a valid file.controls <- controls[,-c(1)]
controls <- as.data.frame(controls)
tot <- cbind(cases, controls)
dim(tot)## [1] 34546 32665tot[1:3, 1:3] ## TTCACGCAGCGAATGC-1_18 ATTATCCCACTGGACC-1_18 TTTACGTCACGACGTC-1_18
## AL627309.1 0 0 0
## AL627309.5 0 0 0
## AL627309.4 0 0 0Transform the count matrix to a Seurat object
# change to Seurat object
data <- CreateSeuratObject(counts = tot, project = "covid") # assign project name
# read in meta data
meta <- read.table("./meta/meta_data_subsetted.txt", h=T, sep="\t")
data <- AddMetaData(data, meta) # add the meta data to the data seurat object
data <- subset(data, subset = nFeature_RNA > 200) # remove low-quality cells with very few genes
data## An object of class Seurat
## 34546 features across 28575 samples within 1 assay
## Active assay: RNA (34546 features, 0 variable features)Add a mitochondrial QC metric to meta.data
data[["percent.mt"]] <- PercentageFeatureSet(data, pattern = "^MT-")
# Visualize QC metrics as a violin plot
VlnPlot(data, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
# FeatureScatter is typically used to visualize feature-feature relationships, but can be used
# for anything calculated by the object, i.e. columns in object metadata, PC scores etc.
plot1 <- FeatureScatter(data, feature1 = "nCount_RNA", feature2 = "percent.mt")
plot2 <- FeatureScatter(data, feature1 = "nCount_RNA", feature2 = "nFeature_RNA")
plot1 + plot2
### Quality control
Cell level and gene level
- Gene (transcript) level
- Removal of genes that are not expressed in more than a few cells
- Dropouts
- Cell level
- Number of counts per barcode (count depth)
- Number of genes per barcode (doublets)
- Fraction of counts from mitochondrial genes per barcode (dying cells)
We filter cells that have unique feature counts over 2,500 or less than 200 We filter cells that have >5% mitochondrial count
data <- subset(data, subset = nFeature_RNA > 200 & nFeature_RNA < 2500 & percent.mt < 5)
data## An object of class Seurat
## 34546 features across 26697 samples within 1 assay
## Active assay: RNA (34546 features, 0 variable features)Data Normalization
Count depth scaling (counts per million CPM) normalization Log transformation
# normalization of feature expression measurement for each cell by total expression
# then multiplied by scale factor (10k by default) and log-transformed
data <- NormalizeData(data, normalization.method = "LogNormalize", scale.factor = 10000)Identifying highly variable features
data <- FindVariableFeatures(data, selection.method = "vst", nfeatures = 2000)
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(data), 10)
# plot variable features with and without labels
plot1 <- VariableFeaturePlot(data)
plot2 <- LabelPoints(plot = plot1, points = top10, repel = TRUE)## When using repel, set xnudge and ynudge to 0 for optimal resultsplot2## Warning: Transformation introduced infinite values in continuous x-axis
Scaling the data:a standard pre-processing step prior to dimensional reduction techniques
all.genes <- rownames(data)
data <- ScaleData(data, features = all.genes)## Centering and scaling data matrixLinear dimension reduction
To reduce noise and facilitate visualization.
data <- RunPCA(data, features = VariableFeatures(object = data))## PC_ 1
## Positive: DNAH12, DNAH9, LRRIQ1, CFAP43, SPAG17, DCDC1, CFAP299, HYDIN, AGBL4, DNAH6
## DNAH3, PACRG, ZBBX, RP1, ARMC3, PTPRT, ERICH3, CFAP47, DNAAF1, LMNTD1
## CFAP46, ADGB, VWA3B, DNAH7, ECT2L, TMEM232, CFAP157, VWA3A, TTC29, AL139815.1
## Negative: CTSB, PSAP, SLC8A1, KYNU, TFRC, PDE4B, CD163, KCNMA1, MSR1, SLC16A10
## FTL, SRGN, APOE, SLCO2B1, CTSD, SLC11A1, LGMN, ITGAX, MRC1, CTSZ
## FTH1, ATP1B3, HLA-DRA, ACSL1, CD74, B2M, GRN, RBPJ, GPNMB, SLC1A3
## PC_ 2
## Positive: KYNU, CTSB, CTSS, ACSL1, PDE4B, KCNMA1, CD163, SLC16A10, MT-CO2, MSR1
## MT-CO1, CD74, SRGN, TFRC, SLCO2B1, MRC1, PSAP, CTSD, SLC11A1, FMN1
## ITGAX, HLA-DRA, THEMIS, LGMN, TOX, RNF144B, CTSZ, GK, TFEC, APOE
## Negative: COL5A2, COL6A3, CACNA1C, COL1A2, PRKG1, COL3A1, LAMA2, CALD1, CDH11, PDZRN3
## GPC6, LSAMP, COL5A1, RBMS3, BICC1, RYR2, DCN, CACNB2, FGF7, SLIT2
## THBS2, COL1A1, FN1, ROR2, DPYSL3, CCDC80, LUM, DLG2, CCDC102B, PDGFRA
## PC_ 3
## Positive: SFTPB, SFTA3, ZNF385B, MECOM, ATP13A4, AL132857.1, ROS1, MAGI3, CADM1, PEBP4
## SHROOM3, LIMCH1, ABCA3, GPC5, LAMA3, NEDD4L, P3H2, RANBP17, GPRC5A, LMO7
## AC044810.2, LMO3, HOPX, AC027288.3, LINC01937, UNC13B, EMP2, AC112206.2, DAPK2, SNX25
## Negative: DNAH12, DNAH9, CFAP299, CFAP43, DNAH3, HYDIN, SPAG17, BICC1, ZBBX, DNAAF1
## ERICH3, ARMC3, CFAP46, ECT2L, LMNTD1, ADGB, CFAP47, NEK10, VWA3B, VWA3A
## CFAP54, PTPRT, COL1A2, CFAP157, COL3A1, DCDC1, COL5A2, AL139815.1, FGF14, TTC29
## PC_ 4
## Positive: FTL, CTSD, PSAP, APOE, CTSB, FTH1, CTSZ, GRN, CTSL, GPNMB
## LGMN, HLA-DRA, CD63, TMSB4X, CCL18, GLUL, CSTB, CD68, ACSL1, IFI30
## C1QA, ACP5, CD74, S100A11, S100A6, C1QB, FMN1, SLC16A10, ACTB, CD81
## Negative: NCKAP5, GALNT18, AL355499.1, RTKN2, NRG3, THEMIS, TIMP3, ATF7IP2, LDB2, COL4A2
## GPM6A, MAP2, LINC01290, KHDRBS2, IFNG-AS1, AC010974.2, CAV1, AC022325.2, STXBP6, GLCCI1
## SYN3, NFATC2, PTPRB, CCDC85A, AC027288.3, CTNND2, SCEL, VWF, ADGRL2, CLIC5
## PC_ 5
## Positive: ROS1, AC096531.2, AGBL1, ACOXL, LRRK2, ABCA3, SFTPC, ERBB4, AC010998.1, LHFPL3
## SCN1A, AFF3, CCDC141, LAMP3, SFTPA1, ARHGEF38, SLC22A3, TMEM163, AC092640.1, SFTPA2
## LHFPL3-AS2, TOX, SFTPB, ZNF385B, DMBT1, PTPRG, AC046195.1, LRP2, ALPL, RMST
## Negative: AL355499.1, NCKAP5, SCEL, RTKN2, AC027288.3, GPM6A, LINC01290, AC022325.2, EMP2, AC010974.2
## CTNND2, FAM189A2, LAMA3, ANKRD29, NCKAP5-AS2, AL359378.1, CAV1, CLIC5, GPRC5D-AS1, MYO16-AS1
## AC002066.1, GPRC5D, KHDRBS2, NRG1, ANOS1, MAP2, GALNT13, HULC, AC044810.2, COL4A3DimPlot(data, reduction = "pca")
ElbowPlot(data)
# Cluster cells ## Groups similar cells based on their transcriptomics
## Using modularity optimization techniques such as the Louvain
algorithm (default)
data <- FindNeighbors(data, dims = 1:10)## Computing nearest neighbor graph## Computing SNNdata <- FindClusters(data, resolution = 0.5)## Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
##
## Number of nodes: 26697
## Number of edges: 868347
##
## Running Louvain algorithm...
## Maximum modularity in 10 random starts: 0.9380
## Number of communities: 16
## Elapsed time: 2 secondsNon-linear dimensional reduction (UMAP/tSNE)
##To learn the underlying manifold of the data in order to place similar cells together in low-dimensional space
# If you haven't installed UMAP, you can do so via reticulate::py_install(packages ='umap-learn')
data <- RunUMAP(data, dims = 1:10)## Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
## To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
## This message will be shown once per session## 12:10:25 UMAP embedding parameters a = 0.9922 b = 1.112## 12:10:25 Read 26697 rows and found 10 numeric columns## 12:10:25 Using Annoy for neighbor search, n_neighbors = 30## 12:10:25 Building Annoy index with metric = cosine, n_trees = 50## 0% 10 20 30 40 50 60 70 80 90 100%## [----|----|----|----|----|----|----|----|----|----|## **************************************************|
## 12:10:27 Writing NN index file to temp file /var/folders/0g/b0krx8616jvfdx7k8f7gcn580000gp/T//RtmppfHsqg/filecb8c11daae
## 12:10:27 Searching Annoy index using 1 thread, search_k = 3000
## 12:10:33 Annoy recall = 100%
## 12:10:33 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
## 12:10:34 Initializing from normalized Laplacian + noise (using irlba)
## 12:10:36 Commencing optimization for 200 epochs, with 1128696 positive edges
## 12:10:43 Optimization finished# note that you can set `label = TRUE` or use the LabelClusters function to help label
# individual clusters
DimPlot(data, reduction = "umap")
# stopping point (save the clustered data)
#saveRDS(data, file = "./data_clustered.rds")Post-clustering analysis
One approach after clustering is to perform differential expression analysis. There are two different differential expression analysis: - Between clusters - Between experimental conditions
Differential expression between clusters
Finding the specific genes that are differentially expressed between cell type clusters allows identification of markers. Seurat has inbuilt functions to find these gene markers, which can help in labeling of the clusters.
The function “FindMarkers” will help in identifying the differentially expressed genes in a cell type cluster.
# for example: find all markers of cluster 2
cluster2.markers <- FindMarkers(data, ident.1 = 2, min.pct = 0.25)## For a more efficient implementation of the Wilcoxon Rank Sum Test,
## (default method for FindMarkers) please install the limma package
## --------------------------------------------
## install.packages('BiocManager')
## BiocManager::install('limma')
## --------------------------------------------
## After installation of limma, Seurat will automatically use the more
## efficient implementation (no further action necessary).
## This message will be shown once per sessionhead(cluster2.markers, n = 5)## p_val avg_log2FC pct.1 pct.2 p_val_adj
## TMEM51 0 1.245006 0.270 0.055 0
## C1QA 0 1.280959 0.261 0.035 0
## THEMIS2 0 1.107038 0.250 0.035 0
## LAPTM5 0 1.088313 0.385 0.088 0
## PDE4B 0 1.961980 0.668 0.158 0VlPlot and FeaturePlot can help visualize localization of marker genes with clusters
VlnPlot(data, features = c("TMEM51", "C1QA"))
FeaturePlot(data, features = c("TMEM51", "C1QA", "THEMIS2", "LAPTM5"))
Find Markers for all cell type clusters
# find markers for every cluster compared to all remaining cells, report only the positive
# ones
data.markers <- FindAllMarkers(data, only.pos = TRUE, min.pct = 0.25, logfc.threshold = 0.25)
data.markers %>%
group_by(cluster) %>%
slice_max(n = 2, order_by = avg_log2FC)## # A tibble: 32 × 7
## # Groups: cluster [16]
## p_val avg_log2FC pct.1 pct.2 p_val_adj cluster gene
## <dbl> <dbl> <dbl> <dbl> <dbl> <fct> <chr>
## 1 0 3.74 0.516 0.018 0 0 THEMIS
## 2 0 3.62 0.715 0.049 0 0 SKAP1
## 3 0 6.14 0.881 0.021 0 1 COL3A1
## 4 0 5.84 0.755 0.011 0 1 COL1A1
## 5 0 3.14 0.772 0.089 0 2 KCNMA1
## 6 0 3.10 0.672 0.081 0 2 TFRC
## 7 0 2.60 0.655 0.161 0 3 SLC8A1
## 8 1.45e-189 2.57 0.331 0.134 5.01e-185 3 VCAN
## 9 0 5.14 0.719 0.032 0 4 ROBO2
## 10 0 4.30 0.624 0.056 0 4 ELN
## # ℹ 22 more rowsAssigning cell type identity to clusters
Once cell type clusters and marker genes are identified, we can assign labels to cell types. At this point, there is no inbuilt cell type labeling in Seurat, so this step is done manually. 💡 The database CellMarker has manually curated gene markers and cell annotations. Link: http://xteam.xbio.top/CellMarker/search.jsp?cellMarkerSpeciesType=Human&cellMarker=ELN
# For this workshop, the cell clusters have already been labeled
DimPlot(object = data,
reduction = "umap",
group.by = "cell_type_main")
Differential expression between conditions
In this case, the goal is to identify those genes that are differentially expressed between two experimental conditions such as (stimulated vs unstimulated cells) or between disease cases and healthy controls in specific cell type. However, having only 3 patients is probably too low, with many more patients it will work better to run pseudobulk analysis.
So another way to look broadly at these changes is to plot the average expression of both the covid and control cells and look for genes that are visual outliers on a scatter plot. Here, we take the average expression of both the covid and control naive T cells and B cell populations and generate the scatter plots, highlighting genes that exhibit dramatic responses to disease condition. To visualize the two disease conditions side-by-side, we can use the split.by argument to show each condition colored by cluster.
# plot this clustering
plot_grid(ncol = 2, DimPlot(data, label = T) + NoAxes(), DimPlot(data, group.by = "disease") + NoAxes())
First we subset our data for the desired cell cluster, then change the cell identities to the variable of comparison (which now in our case is the “disease”, e.g. Covid/Ctrl).❗️Since we are using the same expression data to do cell type marker identification and differential expression, this is prone to double dipping. If you want to learn more about how to resolve this issue, Link : https://doi.org/10.21203/rs.3.rs-3211191/v1
# select all cells in cluster 1
cell_selection <- subset(data, seurat_clusters == 2)
cell_selection <- SetIdent(cell_selection, value = "disease")
# Compute differential expression
DGE_cell_selection <- FindAllMarkers(cell_selection, log2FC.threshold = 0.2, test.use = "wilcox",
min.pct = 0.1, min.diff.pct = 0.2, only.pos = TRUE, max.cells.per.ident = 50,
assay = "RNA")## Calculating cluster COVID-19## Calculating cluster normal❗️Imbalanced sex in the subjects can introduce bias during DEG analysis. Make sure to remove if data is available for sex chromosome related genes.
DGE_cell_selection %>%
group_by(cluster) %>%
top_n(-5, p_val) -> top5_cell_selection
VlnPlot(cell_selection, features = as.character(unique(top5_cell_selection$gene)),
ncol = 5, group.by = "disease", assay = "RNA", pt.size = 0.1)
Batch effects
When testing DEG across conditions, sample differences can create a bias. So first, let's check how the top DGEs are expressed across the individuals:
VlnPlot(cell_selection, group.by = "rds.biosample_id", features = as.character(unique(top5_cell_selection$gene)),
ncol = 5, assay = "RNA", pt.size = 0)
❗️It happens here, that many of the genes are evenly expressed across samples. In the case that DEG results are dominated by a single sample, one approach is to use the "downsample" Seurat function to make sure that every sample has the same number of cells.
Plot as dotplot, for whole the dataset:
# Define as Covid or Ctrl in the df and add a gene column
DGE_cell_selection$direction = ifelse(DGE_cell_selection$avg_log2FC > 0, "Covid",
"Ctrl")
DGE_cell_selection$gene = rownames(DGE_cell_selection)
DGE_cell_selection %>%
group_by(direction) %>%
top_n(-20, p_val) -> top20_cell_selection
DotPlot(cell_selection, features = rev(as.character(unique(top20_cell_selection$gene))),
group.by = "rds.biosample_id", assay = "RNA") + coord_flip()
Gene Set Analysis
If interested in checking the function of the differentially expressed genes identified above, you can use hypergeometric enrichment test
# Check available databases to perform enrichment (then choose one)
enrichR::listEnrichrDbs()## geneCoverage genesPerTerm
## 1 13362 275
## 2 27884 1284
## 3 6002 77
## 4 47172 1370
## 5 47107 509
## 6 21493 3713
## 7 1295 18
## 8 3185 73
## 9 2854 34
## 10 15057 300
## 11 4128 48
## 12 34061 641
## 13 7504 155
## 14 16399 247
## 15 12753 57
## 16 23726 127
## 17 32740 85
## 18 13373 258
## 19 19270 388
## 20 13236 82
## 21 14264 58
## 22 3096 31
## 23 22288 4368
## 24 4533 37
## 25 10231 158
## 26 2741 5
## 27 5655 342
## 28 10406 715
## 29 10493 200
## 30 11251 100
## 31 8695 100
## 32 1759 25
## 33 2178 89
## 34 851 15
## 35 10061 106
## 36 11250 166
## 37 15406 300
## 38 17711 300
## 39 17576 300
## 40 15797 176
## 41 12232 343
## 42 13572 301
## 43 6454 301
## 44 3723 47
## 45 7588 35
## 46 7682 78
## 47 7324 172
## 48 8469 122
## 49 13121 305
## 50 26382 1811
## 51 29065 2123
## 52 280 9
## 53 13877 304
## 54 15852 912
## 55 4320 129
## 56 4271 128
## 57 10496 201
## 58 1678 21
## 59 756 12
## 60 3800 48
## 61 2541 39
## 62 1918 39
## 63 5863 51
## 64 6768 47
## 65 25651 807
## 66 19129 1594
## 67 23939 293
## 68 23561 307
## 69 23877 302
## 70 15886 9
## 71 24350 299
## 72 3102 25
## 73 31132 298
## 74 30832 302
## 75 48230 1429
## 76 5613 36
## 77 9559 73
## 78 9448 63
## 79 16725 1443
## 80 19249 1443
## 81 15090 282
## 82 16129 292
## 83 15309 308
## 84 15103 318
## 85 15022 290
## 86 15676 310
## 87 15854 279
## 88 15015 321
## 89 3788 159
## 90 3357 153
## 91 12668 300
## 92 12638 300
## 93 8973 64
## 94 7010 87
## 95 5966 51
## 96 15562 887
## 97 17850 300
## 98 17660 300
## 99 1348 19
## 100 934 13
## 101 2541 39
## 102 2041 42
## 103 5209 300
## 104 49238 1550
## 105 2243 19
## 106 19586 545
## 107 22440 505
## 108 8184 24
## 109 18329 161
## 110 15755 28
## 111 10271 22
## 112 10427 38
## 113 10601 25
## 114 13822 21
## 115 8002 143
## 116 10089 45
## 117 13247 49
## 118 21809 2316
## 119 23601 2395
## 120 20883 299
## 121 19612 299
## 122 25983 299
## 123 19500 137
## 124 14893 128
## 125 17598 1208
## 126 5902 109
## 127 12486 299
## 128 1073 100
## 129 19513 117
## 130 14433 36
## 131 8655 61
## 132 11459 39
## 133 19741 270
## 134 27360 802
## 135 13072 26
## 136 13464 45
## 137 13787 200
## 138 13929 200
## 139 16964 200
## 140 17258 200
## 141 10352 58
## 142 10471 76
## 143 12419 491
## 144 19378 37
## 145 6201 45
## 146 4558 54
## 147 3264 22
## 148 7802 92
## 149 8551 98
## 150 12444 23
## 151 9000 20
## 152 7744 363
## 153 6204 387
## 154 13420 32
## 155 14148 122
## 156 9813 49
## 157 1397 13
## 158 9116 22
## 159 17464 63
## 160 394 73
## 161 11851 586
## 162 8189 421
## 163 18704 100
## 164 5605 39
## 165 5718 31
## 166 14156 40
## 167 16979 295
## 168 4383 146
## 169 54974 483
## 170 12118 448
## 171 12361 124
## 172 9763 139
## 173 8078 102
## 174 7173 43
## 175 5833 100
## 176 14937 33
## 177 11497 80
## 178 11936 34
## 179 9767 33
## 180 14167 80
## 181 17851 102
## 182 16853 360
## 183 6654 136
## 184 1683 10
## 185 20414 112
## 186 26076 250
## 187 26338 250
## 188 25381 250
## 189 25409 250
## 190 11980 250
## 191 31158 805
## 192 30006 815
## 193 13370 103
## 194 13697 343
## 195 2183 18
## 196 12765 13
## 197 1509 100
## 198 18365 1214
## 199 13525 175
## 200 9525 245
## 201 9440 245
## 202 3857 80
## 203 10489 61
## 204 1198 23
## 205 1882 47
## 206 1552 16
## 207 6713 68
## 208 936 15
## 209 8220 146
## 210 9021 793
## 211 8076 96
## 212 14698 33
## 213 10972 85
## 214 12126 38
## 215 13662 12
## 216 18290 34
## 217 12081 50
## 218 12853 485
## 219 3712 9
## 220 19178 218
## 221 19434 369
## 222 19379 250
## 223 10428 115
## 224 8044 42
## libraryName
## 1 Genome_Browser_PWMs
## 2 TRANSFAC_and_JASPAR_PWMs
## 3 Transcription_Factor_PPIs
## 4 ChEA_2013
## 5 Drug_Perturbations_from_GEO_2014
## 6 ENCODE_TF_ChIP-seq_2014
## 7 BioCarta_2013
## 8 Reactome_2013
## 9 WikiPathways_2013
## 10 Disease_Signatures_from_GEO_up_2014
## 11 KEGG_2013
## 12 TF-LOF_Expression_from_GEO
## 13 TargetScan_microRNA
## 14 PPI_Hub_Proteins
## 15 GO_Molecular_Function_2015
## 16 GeneSigDB
## 17 Chromosome_Location
## 18 Human_Gene_Atlas
## 19 Mouse_Gene_Atlas
## 20 GO_Cellular_Component_2015
## 21 GO_Biological_Process_2015
## 22 Human_Phenotype_Ontology
## 23 Epigenomics_Roadmap_HM_ChIP-seq
## 24 KEA_2013
## 25 NURSA_Human_Endogenous_Complexome
## 26 CORUM
## 27 SILAC_Phosphoproteomics
## 28 MGI_Mammalian_Phenotype_Level_3
## 29 MGI_Mammalian_Phenotype_Level_4
## 30 Old_CMAP_up
## 31 Old_CMAP_down
## 32 OMIM_Disease
## 33 OMIM_Expanded
## 34 VirusMINT
## 35 MSigDB_Computational
## 36 MSigDB_Oncogenic_Signatures
## 37 Disease_Signatures_from_GEO_down_2014
## 38 Virus_Perturbations_from_GEO_up
## 39 Virus_Perturbations_from_GEO_down
## 40 Cancer_Cell_Line_Encyclopedia
## 41 NCI-60_Cancer_Cell_Lines
## 42 Tissue_Protein_Expression_from_ProteomicsDB
## 43 Tissue_Protein_Expression_from_Human_Proteome_Map
## 44 HMDB_Metabolites
## 45 Pfam_InterPro_Domains
## 46 GO_Biological_Process_2013
## 47 GO_Cellular_Component_2013
## 48 GO_Molecular_Function_2013
## 49 Allen_Brain_Atlas_up
## 50 ENCODE_TF_ChIP-seq_2015
## 51 ENCODE_Histone_Modifications_2015
## 52 Phosphatase_Substrates_from_DEPOD
## 53 Allen_Brain_Atlas_down
## 54 ENCODE_Histone_Modifications_2013
## 55 Achilles_fitness_increase
## 56 Achilles_fitness_decrease
## 57 MGI_Mammalian_Phenotype_2013
## 58 BioCarta_2015
## 59 HumanCyc_2015
## 60 KEGG_2015
## 61 NCI-Nature_2015
## 62 Panther_2015
## 63 WikiPathways_2015
## 64 Reactome_2015
## 65 ESCAPE
## 66 HomoloGene
## 67 Disease_Perturbations_from_GEO_down
## 68 Disease_Perturbations_from_GEO_up
## 69 Drug_Perturbations_from_GEO_down
## 70 Genes_Associated_with_NIH_Grants
## 71 Drug_Perturbations_from_GEO_up
## 72 KEA_2015
## 73 Gene_Perturbations_from_GEO_up
## 74 Gene_Perturbations_from_GEO_down
## 75 ChEA_2015
## 76 dbGaP
## 77 LINCS_L1000_Chem_Pert_up
## 78 LINCS_L1000_Chem_Pert_down
## 79 GTEx_Tissue_Expression_Down
## 80 GTEx_Tissue_Expression_Up
## 81 Ligand_Perturbations_from_GEO_down
## 82 Aging_Perturbations_from_GEO_down
## 83 Aging_Perturbations_from_GEO_up
## 84 Ligand_Perturbations_from_GEO_up
## 85 MCF7_Perturbations_from_GEO_down
## 86 MCF7_Perturbations_from_GEO_up
## 87 Microbe_Perturbations_from_GEO_down
## 88 Microbe_Perturbations_from_GEO_up
## 89 LINCS_L1000_Ligand_Perturbations_down
## 90 LINCS_L1000_Ligand_Perturbations_up
## 91 L1000_Kinase_and_GPCR_Perturbations_down
## 92 L1000_Kinase_and_GPCR_Perturbations_up
## 93 Reactome_2016
## 94 KEGG_2016
## 95 WikiPathways_2016
## 96 ENCODE_and_ChEA_Consensus_TFs_from_ChIP-X
## 97 Kinase_Perturbations_from_GEO_down
## 98 Kinase_Perturbations_from_GEO_up
## 99 BioCarta_2016
## 100 HumanCyc_2016
## 101 NCI-Nature_2016
## 102 Panther_2016
## 103 DrugMatrix
## 104 ChEA_2016
## 105 huMAP
## 106 Jensen_TISSUES
## 107 RNA-Seq_Disease_Gene_and_Drug_Signatures_from_GEO
## 108 MGI_Mammalian_Phenotype_2017
## 109 Jensen_COMPARTMENTS
## 110 Jensen_DISEASES
## 111 BioPlex_2017
## 112 GO_Cellular_Component_2017
## 113 GO_Molecular_Function_2017
## 114 GO_Biological_Process_2017
## 115 GO_Cellular_Component_2017b
## 116 GO_Molecular_Function_2017b
## 117 GO_Biological_Process_2017b
## 118 ARCHS4_Tissues
## 119 ARCHS4_Cell-lines
## 120 ARCHS4_IDG_Coexp
## 121 ARCHS4_Kinases_Coexp
## 122 ARCHS4_TFs_Coexp
## 123 SysMyo_Muscle_Gene_Sets
## 124 miRTarBase_2017
## 125 TargetScan_microRNA_2017
## 126 Enrichr_Libraries_Most_Popular_Genes
## 127 Enrichr_Submissions_TF-Gene_Coocurrence
## 128 Data_Acquisition_Method_Most_Popular_Genes
## 129 DSigDB
## 130 GO_Biological_Process_2018
## 131 GO_Cellular_Component_2018
## 132 GO_Molecular_Function_2018
## 133 TF_Perturbations_Followed_by_Expression
## 134 Chromosome_Location_hg19
## 135 NIH_Funded_PIs_2017_Human_GeneRIF
## 136 NIH_Funded_PIs_2017_Human_AutoRIF
## 137 Rare_Diseases_AutoRIF_ARCHS4_Predictions
## 138 Rare_Diseases_GeneRIF_ARCHS4_Predictions
## 139 NIH_Funded_PIs_2017_AutoRIF_ARCHS4_Predictions
## 140 NIH_Funded_PIs_2017_GeneRIF_ARCHS4_Predictions
## 141 Rare_Diseases_GeneRIF_Gene_Lists
## 142 Rare_Diseases_AutoRIF_Gene_Lists
## 143 SubCell_BarCode
## 144 GWAS_Catalog_2019
## 145 WikiPathways_2019_Human
## 146 WikiPathways_2019_Mouse
## 147 TRRUST_Transcription_Factors_2019
## 148 KEGG_2019_Human
## 149 KEGG_2019_Mouse
## 150 InterPro_Domains_2019
## 151 Pfam_Domains_2019
## 152 DepMap_WG_CRISPR_Screens_Broad_CellLines_2019
## 153 DepMap_WG_CRISPR_Screens_Sanger_CellLines_2019
## 154 MGI_Mammalian_Phenotype_Level_4_2019
## 155 UK_Biobank_GWAS_v1
## 156 BioPlanet_2019
## 157 ClinVar_2019
## 158 PheWeb_2019
## 159 DisGeNET
## 160 HMS_LINCS_KinomeScan
## 161 CCLE_Proteomics_2020
## 162 ProteomicsDB_2020
## 163 lncHUB_lncRNA_Co-Expression
## 164 Virus-Host_PPI_P-HIPSTer_2020
## 165 Elsevier_Pathway_Collection
## 166 Table_Mining_of_CRISPR_Studies
## 167 COVID-19_Related_Gene_Sets
## 168 MSigDB_Hallmark_2020
## 169 Enrichr_Users_Contributed_Lists_2020
## 170 TG_GATES_2020
## 171 Allen_Brain_Atlas_10x_scRNA_2021
## 172 Descartes_Cell_Types_and_Tissue_2021
## 173 KEGG_2021_Human
## 174 WikiPathway_2021_Human
## 175 HuBMAP_ASCT_plus_B_augmented_w_RNAseq_Coexpression
## 176 GO_Biological_Process_2021
## 177 GO_Cellular_Component_2021
## 178 GO_Molecular_Function_2021
## 179 MGI_Mammalian_Phenotype_Level_4_2021
## 180 CellMarker_Augmented_2021
## 181 Orphanet_Augmented_2021
## 182 COVID-19_Related_Gene_Sets_2021
## 183 PanglaoDB_Augmented_2021
## 184 Azimuth_Cell_Types_2021
## 185 PhenGenI_Association_2021
## 186 RNAseq_Automatic_GEO_Signatures_Human_Down
## 187 RNAseq_Automatic_GEO_Signatures_Human_Up
## 188 RNAseq_Automatic_GEO_Signatures_Mouse_Down
## 189 RNAseq_Automatic_GEO_Signatures_Mouse_Up
## 190 GTEx_Aging_Signatures_2021
## 191 HDSigDB_Human_2021
## 192 HDSigDB_Mouse_2021
## 193 HuBMAP_ASCTplusB_augmented_2022
## 194 FANTOM6_lncRNA_KD_DEGs
## 195 MAGMA_Drugs_and_Diseases
## 196 PFOCR_Pathways
## 197 Tabula_Sapiens
## 198 ChEA_2022
## 199 Diabetes_Perturbations_GEO_2022
## 200 LINCS_L1000_Chem_Pert_Consensus_Sigs
## 201 LINCS_L1000_CRISPR_KO_Consensus_Sigs
## 202 Tabula_Muris
## 203 Reactome_2022
## 204 SynGO_2022
## 205 GlyGen_Glycosylated_Proteins_2022
## 206 IDG_Drug_Targets_2022
## 207 KOMP2_Mouse_Phenotypes_2022
## 208 Metabolomics_Workbench_Metabolites_2022
## 209 Proteomics_Drug_Atlas_2023
## 210 The_Kinase_Library_2023
## 211 GTEx_Tissues_V8_2023
## 212 GO_Biological_Process_2023
## 213 GO_Cellular_Component_2023
## 214 GO_Molecular_Function_2023
## 215 PFOCR_Pathways_2023
## 216 GWAS_Catalog_2023
## 217 GeDiPNet_2023
## 218 MAGNET_2023
## 219 Azimuth_2023
## 220 Rummagene_kinases
## 221 Rummagene_signatures
## 222 Rummagene_transcription_factors
## 223 MoTrPAC_2023
## 224 WikiPathway_2023_Human
## link
## 1 http://hgdownload.cse.ucsc.edu/goldenPath/hg18/database/
## 2 http://jaspar.genereg.net/html/DOWNLOAD/
## 3
## 4 http://amp.pharm.mssm.edu/lib/cheadownload.jsp
## 5 http://www.ncbi.nlm.nih.gov/geo/
## 6 http://genome.ucsc.edu/ENCODE/downloads.html
## 7 https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways
## 8 http://www.reactome.org/download/index.html
## 9 http://www.wikipathways.org/index.php/Download_Pathways
## 10 http://www.ncbi.nlm.nih.gov/geo/
## 11 http://www.kegg.jp/kegg/download/
## 12 http://www.ncbi.nlm.nih.gov/geo/
## 13 http://www.targetscan.org/cgi-bin/targetscan/data_download.cgi?db=vert_61
## 14 http://amp.pharm.mssm.edu/X2K
## 15 http://www.geneontology.org/GO.downloads.annotations.shtml
## 16 https://pubmed.ncbi.nlm.nih.gov/22110038/
## 17 http://software.broadinstitute.org/gsea/msigdb/index.jsp
## 18 http://biogps.org/downloads/
## 19 http://biogps.org/downloads/
## 20 http://www.geneontology.org/GO.downloads.annotations.shtml
## 21 http://www.geneontology.org/GO.downloads.annotations.shtml
## 22 http://www.human-phenotype-ontology.org/
## 23 http://www.roadmapepigenomics.org/
## 24 http://amp.pharm.mssm.edu/lib/keacommandline.jsp
## 25 https://www.nursa.org/nursa/index.jsf
## 26 http://mips.helmholtz-muenchen.de/genre/proj/corum/
## 27 http://amp.pharm.mssm.edu/lib/keacommandline.jsp
## 28 http://www.informatics.jax.org/
## 29 http://www.informatics.jax.org/
## 30 http://www.broadinstitute.org/cmap/
## 31 http://www.broadinstitute.org/cmap/
## 32 http://www.omim.org/downloads
## 33 http://www.omim.org/downloads
## 34 http://mint.bio.uniroma2.it/download.html
## 35 http://www.broadinstitute.org/gsea/msigdb/collections.jsp
## 36 http://www.broadinstitute.org/gsea/msigdb/collections.jsp
## 37 http://www.ncbi.nlm.nih.gov/geo/
## 38 http://www.ncbi.nlm.nih.gov/geo/
## 39 http://www.ncbi.nlm.nih.gov/geo/
## 40 https://portals.broadinstitute.org/ccle/home\n
## 41 http://biogps.org/downloads/
## 42 https://www.proteomicsdb.org/
## 43 http://www.humanproteomemap.org/index.php
## 44 http://www.hmdb.ca/downloads
## 45 ftp://ftp.ebi.ac.uk/pub/databases/interpro/
## 46 http://www.geneontology.org/GO.downloads.annotations.shtml
## 47 http://www.geneontology.org/GO.downloads.annotations.shtml
## 48 http://www.geneontology.org/GO.downloads.annotations.shtml
## 49 http://www.brain-map.org/
## 50 http://genome.ucsc.edu/ENCODE/downloads.html
## 51 http://genome.ucsc.edu/ENCODE/downloads.html
## 52 http://www.koehn.embl.de/depod/
## 53 http://www.brain-map.org/
## 54 http://genome.ucsc.edu/ENCODE/downloads.html
## 55 http://www.broadinstitute.org/achilles
## 56 http://www.broadinstitute.org/achilles
## 57 http://www.informatics.jax.org/
## 58 https://cgap.nci.nih.gov/Pathways/BioCarta_Pathways
## 59 http://humancyc.org/
## 60 http://www.kegg.jp/kegg/download/
## 61 http://pid.nci.nih.gov/
## 62 http://www.pantherdb.org/
## 63 http://www.wikipathways.org/index.php/Download_Pathways
## 64 http://www.reactome.org/download/index.html
## 65 http://www.maayanlab.net/ESCAPE/
## 66 http://www.ncbi.nlm.nih.gov/homologene
## 67 http://www.ncbi.nlm.nih.gov/geo/
## 68 http://www.ncbi.nlm.nih.gov/geo/
## 69 http://www.ncbi.nlm.nih.gov/geo/
## 70 https://grants.nih.gov/grants/oer.htm\n
## 71 http://www.ncbi.nlm.nih.gov/geo/
## 72 http://amp.pharm.mssm.edu/Enrichr
## 73 http://www.ncbi.nlm.nih.gov/geo/
## 74 http://www.ncbi.nlm.nih.gov/geo/
## 75 http://amp.pharm.mssm.edu/Enrichr
## 76 http://www.ncbi.nlm.nih.gov/gap
## 77 https://clue.io/
## 78 https://clue.io/
## 79 http://www.gtexportal.org/
## 80 http://www.gtexportal.org/
## 81 http://www.ncbi.nlm.nih.gov/geo/
## 82 http://www.ncbi.nlm.nih.gov/geo/
## 83 http://www.ncbi.nlm.nih.gov/geo/
## 84 http://www.ncbi.nlm.nih.gov/geo/
## 85 http://www.ncbi.nlm.nih.gov/geo/
## 86 http://www.ncbi.nlm.nih.gov/geo/
## 87 http://www.ncbi.nlm.nih.gov/geo/
## 88 http://www.ncbi.nlm.nih.gov/geo/
## 89 https://clue.io/
## 90 https://clue.io/
## 91 https://clue.io/
## 92 https://clue.io/
## 93 http://www.reactome.org/download/index.html
## 94 http://www.kegg.jp/kegg/download/
## 95 http://www.wikipathways.org/index.php/Download_Pathways
## 96
## 97 http://www.ncbi.nlm.nih.gov/geo/
## 98 http://www.ncbi.nlm.nih.gov/geo/
## 99 http://cgap.nci.nih.gov/Pathways/BioCarta_Pathways
## 100 http://humancyc.org/
## 101 http://pid.nci.nih.gov/
## 102 http://www.pantherdb.org/pathway/
## 103 https://ntp.niehs.nih.gov/drugmatrix/
## 104 http://amp.pharm.mssm.edu/Enrichr
## 105 http://proteincomplexes.org/
## 106 http://tissues.jensenlab.org/
## 107 http://www.ncbi.nlm.nih.gov/geo/
## 108 http://www.informatics.jax.org/
## 109 http://compartments.jensenlab.org/
## 110 http://diseases.jensenlab.org/
## 111 http://bioplex.hms.harvard.edu/
## 112 http://www.geneontology.org/
## 113 http://www.geneontology.org/
## 114 http://www.geneontology.org/
## 115 http://www.geneontology.org/
## 116 http://www.geneontology.org/
## 117 http://www.geneontology.org/
## 118 http://amp.pharm.mssm.edu/archs4
## 119 http://amp.pharm.mssm.edu/archs4
## 120 http://amp.pharm.mssm.edu/archs4
## 121 http://amp.pharm.mssm.edu/archs4
## 122 http://amp.pharm.mssm.edu/archs4
## 123 http://sys-myo.rhcloud.com/
## 124 http://mirtarbase.mbc.nctu.edu.tw/
## 125 http://www.targetscan.org/
## 126 http://amp.pharm.mssm.edu/Enrichr
## 127 http://amp.pharm.mssm.edu/Enrichr
## 128 http://amp.pharm.mssm.edu/Enrichr
## 129 http://tanlab.ucdenver.edu/DSigDB/DSigDBv1.0/
## 130 http://www.geneontology.org/
## 131 http://www.geneontology.org/
## 132 http://www.geneontology.org/
## 133 http://www.ncbi.nlm.nih.gov/geo/
## 134 http://hgdownload.cse.ucsc.edu/downloads.html
## 135 https://www.ncbi.nlm.nih.gov/pubmed/
## 136 https://www.ncbi.nlm.nih.gov/pubmed/
## 137 https://amp.pharm.mssm.edu/geneshot/
## 138 https://www.ncbi.nlm.nih.gov/gene/about-generif
## 139 https://www.ncbi.nlm.nih.gov/pubmed/
## 140 https://www.ncbi.nlm.nih.gov/pubmed/
## 141 https://www.ncbi.nlm.nih.gov/gene/about-generif
## 142 https://amp.pharm.mssm.edu/geneshot/
## 143 http://www.subcellbarcode.org/
## 144 https://www.ebi.ac.uk/gwas
## 145 https://www.wikipathways.org/
## 146 https://www.wikipathways.org/
## 147 https://www.grnpedia.org/trrust/
## 148 https://www.kegg.jp/
## 149 https://www.kegg.jp/
## 150 https://www.ebi.ac.uk/interpro/
## 151 https://pfam.xfam.org/
## 152 https://depmap.org/
## 153 https://depmap.org/
## 154 http://www.informatics.jax.org/
## 155 https://www.ukbiobank.ac.uk/tag/gwas/
## 156 https://tripod.nih.gov/bioplanet/
## 157 https://www.ncbi.nlm.nih.gov/clinvar/
## 158 http://pheweb.sph.umich.edu/
## 159 https://www.disgenet.org
## 160 http://lincs.hms.harvard.edu/kinomescan/
## 161 https://portals.broadinstitute.org/ccle
## 162 https://www.proteomicsdb.org/
## 163 https://amp.pharm.mssm.edu/lnchub/
## 164 http://phipster.org/
## 165 http://www.transgene.ru/disease-pathways/
## 166
## 167 https://amp.pharm.mssm.edu/covid19
## 168 https://www.gsea-msigdb.org/gsea/msigdb/collections.jsp
## 169 https://maayanlab.cloud/Enrichr
## 170 https://toxico.nibiohn.go.jp/english/
## 171 https://portal.brain-map.org/
## 172 https://descartes.brotmanbaty.org/bbi/human-gene-expression-during-development/
## 173 https://www.kegg.jp/
## 174 https://www.wikipathways.org/
## 175 https://hubmapconsortium.github.io/ccf-asct-reporter/
## 176 http://www.geneontology.org/
## 177 http://www.geneontology.org/
## 178 http://www.geneontology.org/
## 179 http://www.informatics.jax.org/
## 180 http://biocc.hrbmu.edu.cn/CellMarker/
## 181 http://www.orphadata.org/
## 182 https://maayanlab.cloud/covid19/
## 183 https://panglaodb.se/
## 184 https://azimuth.hubmapconsortium.org/
## 185 https://www.ncbi.nlm.nih.gov/gap/phegeni
## 186 https://maayanlab.cloud/archs4/
## 187 https://maayanlab.cloud/archs4/
## 188 https://maayanlab.cloud/archs4/
## 189 https://maayanlab.cloud/archs4/
## 190 https://gtexportal.org/
## 191 https://www.hdinhd.org/
## 192 https://www.hdinhd.org/
## 193 https://hubmapconsortium.github.io/ccf-asct-reporter/
## 194 https://fantom.gsc.riken.jp/6/
## 195 https://github.com/nybell/drugsets/tree/main/DATA/GENESETS
## 196 https://pfocr.wikipathways.org/
## 197 https://tabula-sapiens-portal.ds.czbiohub.org/
## 198 https://maayanlab.cloud/chea3/
## 199 https://appyters.maayanlab.cloud/#/Gene_Expression_T2D_Signatures
## 200 https://maayanlab.cloud/sigcom-lincs/#/Download
## 201 https://maayanlab.cloud/sigcom-lincs/#/Download
## 202 https://tabula-muris.ds.czbiohub.org/
## 203 https://reactome.org/download-data
## 204 https://www.syngoportal.org/
## 205 https://www.glygen.org/
## 206 https://drugcentral.org/
## 207 https://www.mousephenotype.org/
## 208 https://www.metabolomicsworkbench.org/
## 209 https://www.nature.com/articles/s41587-022-01539-0
## 210 https://kinase-library.phosphosite.org/site
## 211 https://gtexportal.org/home/
## 212 http://www.geneontology.org/
## 213 http://www.geneontology.org/
## 214 http://www.geneontology.org/
## 215 https://pfocr.wikipathways.org/
## 216 https://www.ebi.ac.uk/gwas
## 217 http://gedipnet.bicnirrh.res.in/
## 218 https://magnet-winterlab.herokuapp.com/
## 219 https://azimuth.hubmapconsortium.org/
## 220 https://rummagene.com/
## 221 https://rummagene.com/
## 222 https://rummagene.com/
## 223 https://motrpac-data.org/
## 224 https://www.wikipathways.org/
## numTerms appyter categoryId
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## 187 4269 8
## 188 4216 8
## 189 4216 8
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## 191 2564 4
## 192 2579 4
## 193 777 5
## 194 350 1
## 195 1395 4
## 196 17326 7
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## 198 757 1
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## 217 2388 4
## 218 72 5
## 219 1425 5
## 220 3810 2
## 221 11130 8
## 222 7244 1
## 223 225 5
## 224 801 2# Perform enrichment
enrich_results <- enrichr(genes = DGE_cell_selection$gene[DGE_cell_selection$avg_log2FC >
0], databases = "GO_Biological_Process_2017b")[[1]]## Uploading data to Enrichr... Done.
## Querying GO_Biological_Process_2017b... Done.
## Parsing results... Done.# Visualize your results using a simple barplot
par(mfrow = c(1, 1), mar = c(3, 25, 2, 1))
barplot(height = -log10(enrich_results$P.value)[10:1], names.arg = enrich_results$Term[10:1],
horiz = TRUE, las = 1, border = FALSE, cex.names = 0.6)
abline(v = c(-log10(0.05)), lty = 2)
abline(v = 0, lty = 1)
Session Info
sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: America/Chicago
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] enrichR_3.2 ggplot2_3.4.3 metap_1.8
## [4] multtest_2.56.0 Biobase_2.60.0 BiocGenerics_0.46.0
## [7] cowplot_1.1.1 patchwork_1.1.3 dplyr_1.1.3
## [10] data.table_1.14.8 SeuratObject_4.1.4 Seurat_4.4.0
##
## loaded via a namespace (and not attached):
## [1] mathjaxr_1.6-0 RColorBrewer_1.1-3 rstudioapi_0.15.0
## [4] jsonlite_1.8.7 magrittr_2.0.3 TH.data_1.1-2
## [7] spatstat.utils_3.0-3 farver_2.1.1 rmarkdown_2.25
## [10] vctrs_0.6.3 ROCR_1.0-11 spatstat.explore_3.2-3
## [13] htmltools_0.5.6 curl_5.0.2 plotrix_3.8-2
## [16] sass_0.4.7 sctransform_0.4.0 parallelly_1.36.0
## [19] KernSmooth_2.23-22 bslib_0.5.1 htmlwidgets_1.6.2
## [22] ica_1.0-3 sandwich_3.0-2 plyr_1.8.8
## [25] plotly_4.10.2 zoo_1.8-12 cachem_1.0.8
## [28] igraph_1.5.1 mime_0.12 lifecycle_1.0.3
## [31] pkgconfig_2.0.3 Matrix_1.6-1.1 R6_2.5.1
## [34] fastmap_1.1.1 rbibutils_2.2.15 fitdistrplus_1.1-11
## [37] future_1.33.0 shiny_1.7.5 numDeriv_2016.8-1.1
## [40] digest_0.6.33 colorspace_2.1-0 tensor_1.5
## [43] irlba_2.3.5.1 labeling_0.4.3 WriteXLS_6.4.0
## [46] progressr_0.14.0 fansi_1.0.4 spatstat.sparse_3.0-2
## [49] httr_1.4.7 TFisher_0.2.0 polyclip_1.10-6
## [52] abind_1.4-5 compiler_4.3.1 withr_2.5.1
## [55] mutoss_0.1-13 MASS_7.3-60 rjson_0.2.21
## [58] tools_4.3.1 lmtest_0.9-40 httpuv_1.6.11
## [61] future.apply_1.11.0 qqconf_1.3.2 goftest_1.2-3
## [64] glue_1.6.2 nlme_3.1-163 promises_1.2.1
## [67] grid_4.3.1 Rtsne_0.16 cluster_2.1.4
## [70] reshape2_1.4.4 generics_0.1.3 gtable_0.3.4
## [73] spatstat.data_3.0-1 tidyr_1.3.0 sn_2.1.1
## [76] sp_2.0-0 utf8_1.2.3 spatstat.geom_3.2-5
## [79] RcppAnnoy_0.0.21 ggrepel_0.9.3 RANN_2.6.1
## [82] pillar_1.9.0 stringr_1.5.0 later_1.3.1
## [85] splines_4.3.1 lattice_0.21-8 survival_3.5-7
## [88] deldir_1.0-9 tidyselect_1.2.0 miniUI_0.1.1.1
## [91] pbapply_1.7-2 knitr_1.44 gridExtra_2.3
## [94] scattermore_1.2 stats4_4.3.1 xfun_0.40
## [97] matrixStats_1.0.0 stringi_1.7.12 lazyeval_0.2.2
## [100] yaml_2.3.7 evaluate_0.22 codetools_0.2-19
## [103] tibble_3.2.1 cli_3.6.1 uwot_0.1.16
## [106] xtable_1.8-4 reticulate_1.32.0 Rdpack_2.5
## [109] munsell_0.5.0 jquerylib_0.1.4 Rcpp_1.0.11
## [112] globals_0.16.2 spatstat.random_3.1-6 png_0.1-8
## [115] parallel_4.3.1 ellipsis_0.3.2 listenv_0.9.0
## [118] viridisLite_0.4.2 mvtnorm_1.2-3 scales_1.2.1
## [121] ggridges_0.5.4 leiden_0.4.3 purrr_1.0.2
## [124] rlang_1.1.1 multcomp_1.4-25 mnormt_2.1.1